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Continuous Hydrologic and Water Quality Monitoring of Vernal Ponds
Published on: November 13, 2017
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Effluent trading planning and its application in water quality management: A factor-interaction perspective
J L Zhang1, Y P Li2, X T Zeng3
1College of Environmental Science and Engineering, Qingdao University, Qingdao, Shandong 266071, China.
Environmental Research
|October 27, 2018
Summary
A new Bayesian framework optimizes effluent trading programs by integrating nutrient modeling and risk analysis. Cross-industry trading offers greater benefits and pollution reduction for effective water quality management.
Area of Science:
- Environmental Science
- Water Resource Management
- Environmental Engineering
Background:
- Effluent trading programs are crucial for managing water quality by allowing pollution reduction trading between sources.
- Existing models often struggle to account for system risks and multiple uncertainties in effluent trading program planning.
- Understanding the complex interactions of trading factors under system risk is essential for effective water quality management.
Purpose of the Study:
- To propose a Bayesian risk-induced interval stochastic modeling framework (BRISF) for planning effluent trading programs.
- To identify interactions of key trading factors and assess system risk under various scenarios.
- To compare the effectiveness of cross-industry versus intra-industry effluent trading schemes.
Main Methods:
- Developed a novel Bayesian risk-induced interval stochastic modeling framework (BRISF).
- Integrated nutrient fate modeling (Soil and Water Assessment Tool - SWAT), Bayesian inference (Random Walk Metropolis algorithm - RWM), and constraint-violation risk-based two-stage stochastic programming (CRTSP).
- Applied the framework to the Xiangxihe watershed in China, analyzing thousands of scenarios to evaluate trading ratios and treatment rates.
Main Results:
- BRISF successfully generated optimal trading schemes for different risk levels.
- Analysis revealed individual and interactive effects of trading ratios and treatment rates on the trading system.
- Cross-industry effluent trading demonstrated higher benefits and significant pollution loading reduction compared to intra-industry trading.
Conclusions:
- The proposed BRISF provides a robust approach for planning effluent trading programs under uncertainty and system risk.
- Cross-industry effluent trading is recommended for achieving optimal water quality management and maximizing system benefits.
- The study highlights the importance of considering risk and interactions of factors for effective environmental policy design.
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